A method using 1H NMR and LC/MS with pattern recognition tools such as principal component analysis (PCA) and orthogonal projection to latent structure discriminant analysis (O-PLS-DA) was used to study the urinary metabolic profiles associated with an increase in urinary albumin in a general population. The normalized peak intensities obtained from 1H NMR and LC/MS with nonparametric two-tailed Mann-Whitney analysis was used for the identification of network of potential biomarkers corresponding to the increase of albumin in urine. The specificity of detecting the stated metabolites by 1H NMR and LC/MS was demonstrated. Our preliminary data obtained demonstrated that LC/MS may produce more distinctive metabolic profiles. For the patient group, changes in alanine, kyneurnic acid, and xanthurenic acid might be associated with changes in the tryptophan metabolism. At the same time, other metabolites that were involved in citric acid cycle, amino acid metabolism, and cellular functions were affected in the patient group. The proposed approach provided a comprehensive picture of the metabolic changes induced by the increase of protein in urine and demonstrated the advantages of using multiple diagnostic biomarkers. At the same time, the current work was demonstrated as a potential cost-effective solution of high-throughput analysis with pattern recognition tools as applied here in a real clinical situation.